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Finding the original mass: A machine learning model and its deployment for lithic scrapers.

Guillermo Bustos-Pérez^1,2,3

¹Departament of Human Origins, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.

|July 28, 2025

Summary

Researchers developed a machine learning model to accurately predict the original mass of stone tools, like scrapers, even after multiple resharpening events. This tool aids in understanding past lithic technology and tool use patterns.

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Area of Science:

Archaeology
Lithic Analysis
Material Science

Background:

Predicting original stone tool mass is crucial for understanding past technological organization and societal patterns.
Previous methods for estimating original mass have shown limited success and were not tested on successive resharpening episodes.

Purpose of the Study:

To develop a precise method for predicting the original mass of retouched scrapers.
To test the efficacy of machine learning models in estimating original flake mass after repeated retouching.

Main Methods:

Experimentally knapped flint flakes were repeatedly resharpened into scrapers.
Attributes including mass, retouch height, thickness, and GIUR index were recorded after each resharpening episode.
Four machine learning models were trained to predict original flake mass.

Main Results:

A Random Forest model achieved high accuracy, with an R-squared value of 0.97 for predicting original flake mass.
The Random Forest model also accurately predicted mass loss due to retouching (R-squared = 0.84).
The model has been implemented in an open-source Shiny app for widespread use.

Conclusions:

Machine learning, particularly the Random Forest model, offers a highly precise solution for predicting the original mass of retouched lithic tools.
This approach overcomes limitations of previous methods and can be applied to archaeological assemblages.
The developed tool facilitates broader research into lithic technology and past human behavior.